Biomarkers of Kidney Failure and All-Cause Mortality in CKD

Background: Chronic kidney disease (CKD) carries a variable risk for multiple adverse outcomes, highlighting the need for a personalised approach. This study evaluated several novel biomarkers linked to key disease mechanisms to predict the risk of kidney failure (first event of eGFR <15 ml/min/1.73m2 or kidney replacement therapy), all-cause mortality, and a composite of both.Methods: We included 2,884 adults with non-dialysis CKD from 16 nephrology centres across the UK. Twenty-one biomarkers associated with kidney damage, fibrosis, inflammation, and cardiovascular disease were analysed in urine, plasma, or serum. Cox proportional hazards models were used to assess biomarker associations and develop risk prediction models.Results: Participants had mean age 63 (15) years, 58% were male and 87% White. Median eGFR 35 (25, 47) ml/min/1.73m2, and median urinary albumin-to-creatinine ratio (UACR) 197 (32, 895) mg/g. During median 48 (33, 55) months follow-up, 680 kidney failure events and 414 all-cause mortality events occurred. For kidney failure, a model combining three biomarkers (sTNFR1, sCD40, UCOL1A1) showed good discrimination (c-index 0.86, 95% CI: 0.83-0.89) but was outperformed by a model using established risk factors (age, sex, ethnicity, eGFR, UACR; c-index 0.90, 95% CI: 0.88-0.92). For all-cause mortality, a model using three biomarkers (hs-cTnT, NT-proBNP, suPAR) demonstrated equivalent discrimination (c-index 0.80, 95% CI: 0.75-0.84) to an established risk factor model (c-index 0.80, 95% CI: 0.76-0.84).For the composite outcome, the biomarker model discrimination (C-index 0.78, 95% CI: 0.76, 0.81) was numerically higher than for established risk factors (C-index 0.77, 95% CI: 0.74, 0.80), and the addition of biomarkers to the established risk factors led to a small but statistically significant improvement in discrimination (C-index 0.80, 95% CI: 0.77, 0.82; p value < 0.01).Conclusions: Risk prediction models incorporating novel biomarkers showed comparable discrimination to established risk factors for kidney failure and all-cause mortality

Liquid Biopsy of Bile based on Targeted Mass Spectrometry for the Diagnosis of Malignant Biliary Strictures

International audienc

Epidemiological Description and Detection of Antimicrobial Resistance in Various Aquatic Sites in Marseille, France

International audienceAntibiotic resistance is a worldwide public health concern and has been associated with reports of elevated mortality. According to the One Health concept, antibiotic resistance genes are transferrable to organisms, and organisms are shared among humans, animals, and the environment. Consequently, aquatic environments are a possible reservoir of bacteria harboring antibiotic resistance genes. In our study, we screened water and wastewater samples for antibiotic resistance genes by culturing samples on different types of agar media. Then, we performed real-time PCR to detect the presence of genes conferring resistance to beta lactams and colistin, followed by standard PCR and gene sequencing for verification. We mainly isolated Enterobacteriaceae from all samples. In water samples, 36 Gram-negative bacterial strains were isolated and identified. We found three extended-spectrum β-lactamase (ESBL)-producing bacteria—Escherichia coli and Enterobacter cloacae strains—harboring the CTX-M and TEM groups. In wastewater samples, we isolated 114 Gram-negative bacterial strains, mainly E. coli, Klebsiella pneumoniae, Citrobacter freundii and Proteus mirabilis strains. Forty-two bacterial strains were ESBL-producing bacteria, and they harbored at least one gene belonging to the CTX-M, SHV, and TEM groups. We also detected carbapenem-resistant genes, including NDM, KPC, and OXA-48, in four isolates of E. coli. This short epidemiological study allowed us to identify new antibiotic resistance genes present in bacterial strains isolated from water in Marseille. This type of surveillance shows the importance of tracking bacterial resistance in aquatic environments

Bile carcinoembryonic cell adhesion molecule 6 (CEAM6) as a biomarker of malignant biliary stenoses

Differentiating malignant from nonmalignant biliary stenoses is challenging. This could be facilitated by the measurement of cancer biomarkers in bile. We aimed at (i) identifying new cancer biomarkers by comparative proteomic analysis of bile collected from patients with a malignant or benign biliary stenosis (exploratory phase) and (ii) verifying the accuracy of the newly identified potential biomarkers for discriminating malignant versus nonmalignant biliary stenoses in a larger group of patients (confirmation phase). Overall, 66 proteins were found overexpressed (ratio>1.5) in at least one cancer condition using proteomic analysis and 7 proteins were increased in all malignant/nonmalignant disease comparisons. Preliminary screening by immunoblot highlighted carcinoembryonic cell adhesion molecule 6 (CEAM6), a cell surface protein overexpressed in many human cancers, as an interesting candidate biomarker. ELISA subsequently confirmed CEAM6 as a potential bile biomarker for distinguishing malignant from benign biliary stenoses with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.92 (specificity 83%, sensitivity 93%, positive predictive value 93%, and negative predictive value 83%). No significant difference in serum CEAM6 level was found between malignant and nonmalignant samples. Combining bile CEAM6 and serum CA19-9 in a panel further improved diagnostic accuracy for malignant stenoses (AUC 0.96, specificity 83%, sensitivity 97%, positive predictive value 93%, and negative predictive value 91%). CEAM6 measurement in bile could be clinically useful to discriminate between malignant and nonmalignant causes of biliary stenosis. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge

Extracellular Vesicles in Bile as Markers of Malignant Biliary Stenoses

Background & Aims Algorithms for diagnosis of malignant common bile duct (CBD) stenoses are complex and lack accuracy. Malignant tumors secrete large numbers of extracellular vesicles (EVs) into surrounding fluids; EVs might therefore serve as biomarkers for diagnosis. We investigated whether concentrations of EVs in bile could discriminate malignant from nonmalignant CBD stenoses. Methods We collected bile and blood samples from 50 patients undergoing therapeutic endoscopic retrograde cholangiopancreatography at university hospitals in Europe for CBD stenosis of malignant (pancreatic cancer, n = 20 or cholangiocarcinoma, n = 5) or nonmalignant (chronic pancreatitis [CP], n = 15) origin. Ten patients with CBD obstruction due to biliary stones were included as controls. EV concentrations in samples were determined by nanoparticle tracking analyses. The discovery cohort comprised the first 10 patients with a diagnosis of pancreatic cancer, based on tissue analysis, and 10 consecutive controls. Using samples from these subjects, we identified a threshold concentration of bile EVs that could best discriminate between patients with pancreatic cancer from controls. We verified the diagnostic performance of bile EV concentration by analyzing samples from the 30 consecutive patients with a diagnosis of malignant (pancreatic cancer or cholangiocarcinoma, n = 15) or nonmalignant (CP, n = 15) CBD stenosis. Samples were compared using the Mann-Whitney test and nonparametric Spearman correlation analysis. Receiver operating characteristic area under the curve was used to determine diagnostic accuracy. Results In both cohorts, the median concentration of EVs was significantly higher in bile samples from patients with malignant CBD stenoses than controls or nonmalignant CBD stenoses (2.41 × 1015 vs 1.60 × 1014 nanoparticles/L in the discovery cohort; P <.0001 and 4.00 × 1015 vs 1.26 × 1014 nanoparticles/L in the verification cohort; P <.0001). A threshold of 9.46 × 1014 nanoparticles/L in bile best distinguished patients with malignant CBD from controls in the discovery cohort. In the verification cohort, this threshold discriminated malignant from nonmalignant CBD stenoses with 100% accuracy. Serum concentration of EVs distinguished patients with malignant vs patients with nonmalignant CBD stenoses with 63.3% diagnostic accuracy. Conclusions Concentration of EVs in bile samples discriminates between patients with malignant vs nonmalignant CBD stenosis with 100% accuracy. Further studies are needed to confirm these findings. Clinical Trial registration no: ISRCTN66835592.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Epidemiological Description and Detection of Antimicrobial Resistance in Various Aquatic Sites in Marseille, France

Antibiotic-resistant bacteria are involved in serious infections in humans. The dissemination of these bacteria in water, which is in close contact with human activities, is a serious problem, especially under the concept of One Health.</jats:p

Biomarkers of Kidney Failure and All-Cause Mortality in CKD

BACKGROUND: Chronic kidney disease (CKD) carries a variable risk for multiple adverse outcomes, highlighting the need for a personalised approach. This study evaluated several novel biomarkers linked to key disease mechanisms to predict the risk of kidney failure (first event of eGFR <15 ml/min/1.73m2 or kidney replacement therapy), all-cause mortality, and a composite of both. METHODS: We included 2,884 adults with non-dialysis CKD from 16 nephrology centres across the UK. Twenty-one biomarkers associated with kidney damage, fibrosis, inflammation, and cardiovascular disease were analysed in urine, plasma, or serum. Cox proportional hazards models were used to assess biomarker associations and develop risk prediction models. RESULTS: Participants had mean age 63 (15) years, 58% were male and 87% White. Median eGFR 35 (25, 47) ml/min/1.73m2, and median urinary albumin-to-creatinine ratio (UACR) 197 (32, 895) mg/g. During median 48 (33, 55) months follow-up, 680 kidney failure events and 414 all-cause mortality events occurred. For kidney failure, a model combining three biomarkers (sTNFR1, sCD40, UCOL1A1) showed good discrimination (c-index 0.86, 95% CI: 0.83-0.89) but was outperformed by a model using established risk factors (age, sex, ethnicity, eGFR, UACR; c-index 0.90, 95% CI: 0.88-0.92). For all-cause mortality, a model using three biomarkers (hs-cTnT, NT-proBNP, suPAR) demonstrated equivalent discrimination (c-index 0.80, 95% CI: 0.75-0.84) to an established risk factor model (c-index 0.80, 95% CI: 0.76-0.84).For the composite outcome, the biomarker model discrimination (C-index 0.78, 95% CI: 0.76, 0.81) was numerically higher than for established risk factors (C-index 0.77, 95% CI: 0.74, 0.80), and the addition of biomarkers to the established risk factors led to a small but statistically significant improvement in discrimination (C-index 0.80, 95% CI: 0.77, 0.82; p value < 0.01). CONCLUSIONS: Risk prediction models incorporating novel biomarkers showed comparable discrimination to established risk factors for kidney failure and all-cause mortality

Biomarkers of kidney failure and all-cause mortality in chronic kidney disease

Background: Chronic kidney disease (CKD) carries a variable risk for multiple adverse outcomes, highlighting the need for a personalised approach. This study evaluated several novel biomarkers linked to key disease mechanisms to predict the risk of kidney failure (first event of eGFR &lt;15 ml/min/1.73m2 or kidney replacement therapy), all-cause mortality, and a composite of both.Methods: We included 2,884 adults with non-dialysis CKD from 16 nephrology centres across the UK. Twenty-one biomarkers associated with kidney damage, fibrosis, inflammation, and cardiovascular disease were analysed in urine, plasma, or serum. Cox proportional hazards models were used to assess biomarker associations and develop risk prediction models.Results: Participants had mean age 63 (15) years, 58% were male and 87% White. Median eGFR 35 (25, 47) ml/min/1.73m2, and median urinary albumin-to-creatinine ratio (UACR) 197 (32, 895) mg/g. During median 48 (33, 55) months follow-up, 680 kidney failure events and 414 all-cause mortality events occurred. For kidney failure, a model combining three biomarkers (sTNFR1, sCD40, UCOL1A1) showed good discrimination (c-index 0.86, 95% CI: 0.83-0.89) but was outperformed by a model using established risk factors (age, sex, ethnicity, eGFR, UACR; c-index 0.90, 95% CI: 0.88-0.92). For all-cause mortality, a model using three biomarkers (hs-cTnT, NT-proBNP, suPAR) demonstrated equivalent discrimination (c-index 0.80, 95% CI: 0.75-0.84) to an established risk factor model (c-index 0.80, 95% CI: 0.76-0.84).For the composite outcome, the biomarker model discrimination (C-index 0.78, 95% CI: 0.76, 0.81) was numerically higher than for established risk factors (C-index 0.77, 95% CI: 0.74, 0.80), and the addition of biomarkers to the established risk factors led to a small but statistically significant improvement in discrimination (C-index 0.80, 95% CI: 0.77, 0.82; p value &lt; 0.01).Conclusions: Risk prediction models incorporating novel biomarkers showed comparable discrimination to established risk factors for kidney failure and all-cause mortality

Biomarkers of kidney failure and all-cause mortality in chronic kidney disease

Background: chronic kidney disease (CKD) carries a variable risk for multiple adverse outcomes, highlighting the need for a personalised approach. This study evaluated several novel biomarkers linked to key disease mechanisms to predict the risk of kidney failure (first event of eGFR &lt;15 ml/min/1.73m2 or kidney replacement therapy), all-cause mortality, and a composite of both.Methods: we included 2,884 adults with non-dialysis CKD from 16 nephrology centres across the UK. Twenty-one biomarkers associated with kidney damage, fibrosis, inflammation, and cardiovascular disease were analysed in urine, plasma, or serum. Cox proportional hazards models were used to assess biomarker associations and develop risk prediction models.Results: participants had mean age 63 (15) years, 58% were male and 87% White. Median eGFR 35 (25, 47) ml/min/1.73m2, and median urinary albumin-to-creatinine ratio (UACR) 197 (32, 895) mg/g. During median 48 (33, 55) months follow-up, 680 kidney failure events and 414 all-cause mortality events occurred. For kidney failure, a model combining three biomarkers (sTNFR1, sCD40, UCOL1A1) showed good discrimination (c-index 0.86, 95% CI: 0.83–0.89) but was outperformed by a model using established risk factors (age, sex, ethnicity, eGFR, UACR; c-index 0.90, 95% CI: 0.88–0.92). For all-cause mortality, a model using three biomarkers (hs-cTnT, NT-proBNP, suPAR) demonstrated equivalent discrimination (c-index 0.80, 95% CI: 0.75–0.84) to an established risk factor model (c-index 0.80, 95% CI: 0.76–0.84).For the composite outcome, the biomarker model discrimination (C-index 0.78, 95% CI: 0.76, 0.81) was numerically higher than for established risk factors (C-index 0.77, 95% CI: 0.74, 0.80), and the addition of biomarkers to the established risk factors led to a small but statistically significant improvement in discrimination (C-index 0.80, 95% CI: 0.77, 0.82; p value &lt; 0.01)Conclusions: risk prediction models incorporating novel biomarkers showed comparable discrimination to established risk factors for kidney failure and all-cause mortality